import sensor, image, time, os, tf, math, uos, gc

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.skip_frames(time=2000)          # Let the camera adjust.

net = None
labels = None
min_confidence = 0.5

try:
    # load the model, alloc the model file on the heap if we have at least 64K free after loading
    net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')

try:
    labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
    raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')


counter = [0,0,0,0,0,0,0,0,0,0,0]
clock = time.clock()

while True:
    clock.tick()  # 记录当前时间

    img = sensor.snapshot()  # 获取图像帧

    detections = net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])
    for obj in detections:
        label = labels[obj.classid()]
        confidence = obj.confidence()
        if confidence > min_confidence:
            counter[label] += 1
        if counter[label] > 10:
            counter[label] = 0
            print("%s" % labels[label])
            [x, y, w, h] = obj.rect()
            center_x = math.floor(x + (w / 2))
            center_y = math.floor(y + (h / 2))
            print('x %d\ty %d' % (center_x, center_y))
            img.draw_circle((center_x, center_y), 12, color=colors[i], thickness=2)
            img.draw_string(center_x + 20, center_y + 20, labels[i], color=colors[i], scale=1)